The video surveillance using CCTV cameras and other types of static camera have become very common these days. As a result, a vast amount of video data captured from these cameras are generating every day. The storage and transmission of this video data proposes a new challenge. Generally, the video data captured from the static cameras include a lot of data when there is no activity in front of the camera, this data is irrelevant to the user. The relevant information include only when there is a change in the foreground with respect to the background. Thus, it would be recommended if only the relevant information is stored and rest other is discarded. By doing this we can reduce the amount of data which need to be stored.
Various techniques and methods have been used for the compression of video data. One of the technique uses detecting and analyzing the background in the video data. Robust detection of moving objects in video streams is a significant issue for video surveillance. The compression of video data can be performed using background subtraction. Background Subtraction (BS) is one of a common and widely used technique for detecting a foreground (the pixels belonging to moving objects in the scene) using the difference between the current frame and a background model. BS methods are used in a variety of applications ranging from video surveillance, change detection to RADAR, SONAR imaging. Existing schemes use traditional approaches for background subtraction like frame differencing, mean filtering, median filtering, linear predictive filtering, Kalman filtering and so on. They focus is mainly on detecting the foreground rather than focusing on the inherent problems of storage/transmission of the video data.
Another method involves analyzing the foreground as moving object, shadow, and ghost by combining the motion information. In this case, the computation cost is relatively expensive for real-time video surveillance systems because of the computation of optical flow. Recently, the mixture of Gaussians method have been used because it can deal with slow lighting changes, periodical motions from clutter background, slow moving objects, long term scene changes, and camera noises. But it cannot adapt to the quick lighting changes and cannot handle shadows very well.
The compressive sensing is one of the recent technique to be used for background subtraction. This method involves mapping the sparse foreground reconstruction to a compressive sensing framework where object silhouettes are recovered directly from the compressed measurements.
Though various other methods have been used for compressing the video data captured from the static camera. None of the method are robust enough and cost effective.